Vortex beams accompanied with orbital angular momentum have attracted significant attention in research fields due to their formidable capabilities in various crucial applications. However, conventional devices for generating vortex beams still suffer from bulky sizes, high cost, and confined performances. Metalens, as an advanced platform to arbitrarily control the optical waves, has promising prospects to address the predicament for conventional devices. Although great progress has been demonstrated in the applications of vortex beams, they are still confronted with fixed functionality after fabrication that severely hinders their application range. In this work, the phase-change material of Ge₂Sb₂Te₅ (GST) is employed to design the meta-atoms to realize tunable optical responses. Moreover, the focused vortex beam can be accomplished by superimposing a helical phase and hyperbolic phase, and the chromatic aberrations in near-infrared (NIR) range can be corrected by introducing an additional phase compensation. And the design strategy is validated by two different metalenses (BAMTF-1 and BAMTF-2). The numerical results indicate that the chromatic aberrations for two metalens can be corrected in 1.33–1.60 μm covering the telecom range. Moreover, the average focusing efficiency of BAMTF-1 is 51.4%, and that of BAMTF-2 is 39.9%, indicating the favorable performances of designed BAMTF. More importantly, their average focal lengths have a relative tuning range of 38.82% and 33.17% by altering the crystallization ratio of GST, respectively. This work may provide a significant scheme for on-chip and tunable devices for NIR imaging and communication systems.

Traditional facial recognition is realized by facial recognition algorithms based on 2D or 3D digital images and has been well developed and has found wide applications in areas related to identification verification. In this work, we propose a novel live face detection (LFD) method by utilizing snapshot spectral imaging technology, which takes advantage of the distinctive reflected spectra from human faces. By employing a computational spectral reconstruction algorithm based on Tikhonov regularization, a rapid and precise spectral reconstruction with a fidelity of over 99% for the color checkers and various types of “face” samples has been achieved. The flat face areas were extracted exactly from the “face” images with Dlib face detection and Euclidean distance selection algorithms. A large quantity of spectra were rapidly reconstructed from the selected areas and compiled into an extensive database. The convolutional neural network model trained on this database demonstrates an excellent capability for predicting different types of “faces” with an accuracy exceeding 98%, and, according to a series of evaluations, the system’s detection time consistently remained under one second, much faster than other spectral imaging LFD methods. Moreover, a pixel-level liveness detection test system is developed and a LFD experiment shows good agreement with theoretical results, which demonstrates the potential of our method to be applied in other recognition fields. The superior performance and compatibility of our method provide an alternative solution for accurate, highly integrated video LFD applications.

The capability of simultaneously capturing spatial and spectral information represents the most distinctive advantage of spectral imaging technology. To expand its applicability to different scenarios, compact and cost-effective spectral imaging systems are necessary. The mosaic spectral imaging sensor is considered a promising technical pathway for future consumer-grade video spectral imaging, as it offers snapshot acquisition along with advantages such as compactness and integration flexibility. However, this technology heavily relies on spectral reconstruction algorithms, many of which suffer from either excessive computational latency or insufficient robustness to noise, so that their practical utility is limited. In this study, we developed a noise calibration protocol for mosaic spectral sensors based on the sensor noise theory as well as a high-fidelity DN simulation algorithm. These efforts enable the efficient construction of a massive and realistic DN-spectrum dataset. Based on this dataset, we further proposed a spectral reconstruction model using a two-dimensional convolutional neural network (2D-CNN). As a result, the newly developed model demonstrates a strong noise robustness and balanced capabilities in reconstructing the samples with substantial high-level noise, and thus achieves an average reconstruction fidelity of 98.37%, which maintains a level of performance that is comparable to or exceeds that of high-fidelity and complex traditional algorithms. The reconstruction RMSE ratio is only 1.87%, which is only half of that of traditional algorithms. Moreover, the full-image reconstruction time is less than 1 second, which is significantly faster than current high-fidelity algorithms that often require tens of minutes. We believe these advancements will enable lightweight spectral imaging systems to achieve accurate real-time spectral imaging in wide scenarios or under harsher lighting conditions.